Customer Churn Prediction

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Mini Project for Data Mining and Analytics (18CSE355T)

Members:

• Aditi Mittal (RA1911003010226)
• Anweasha Saha (RA1911003010235)
• R. Vijay (RA1911003010239)

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Mini Project Work

• Dataset
• Jupyter Notebook
• Project Report
• PowerPoint Presentation

Overview

• In this project, we have used customer credit card data and customer churn has been predicted on its basis.
• The data included features like Credit Score, Age, Gender, Tenure, Balance, Estimated Salary, etc.
• After doing Exploratory Data Analysis (EDA) of the dataset, LabelEncoder was used to encode Gender and other categorical data.
• Finally, this data was used to train Decision Tree Model and Random Forest Model.
• The random forest model was more effective in its prediction than Decision Tree with a slightly better F1 score.

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WEB APPLICATION - Customer Churn Prediction